ShapRFECV - Recursive Feature Elimination using SHAP importance¶

Recursive Feature Elimination allows you to efficiently reduce the number of features in your dataset, without losing the predictive power of the model. probatus implements the following feature elimination routine for tree-based & linear models:

While any features left, iterate:
    1. (Optional) Tune hyperparameters, in case sklearn compatible search CV e.g. `GridSearchCV` or
        `RandomizedSearchCV` or `BayesSearchCV`are passed as model,
    2. Calculate SHAP feature importance using Cross-Validation,
    3. Remove `step` lowest importance features.

The functionality is similar to RFECV, yet it removes the lowest importance features, based on SHAP features importance. It also supports the use of any hyperparameter search schema that is consistent with sklearn API e.g. GridSearchCV, RandomizedSearchCV and BayesSearchCV passed as a model, thanks to which you can perform hyperparameter optimization at each step of the search. hyperparameters of the model at each round, to tune the model for each features set. Lastly, it supports categorical features (object and category dtype) and missing values in the data, as long as the model supports them.

The main advantages of using this routine are:

• It uses a tree-based or a linear model to detect the complex relations between features and the target.
• It uses SHAP importance, which is one of the most reliable ways to estimate features importance. Unlike many other techniques, it works with missing values and categorical variables.
• Supports the use of sklearn compatible hyperparameter search schemas e.g. GridSearchCV, RandomizedSearchCV and BayesSearchCV, in order to optimize hyperparameters at each iteration. This way you can assess if the removal of a given feature reduces the predictive power, or simply requires additional tuning of the model.
• You can also provide a list of features that should not be eliminated e.g. incase of prior knowledge.

The disadvantages are:

• Removing lowest SHAP importance feature does not always translate to choosing the feature with the lowest impact on a model's performance. Shap importance illustrates how strongly a given feature affects the output of the model, while disregarding correctness of this prediction.
• Currently, the functionality only supports tree-based & linear binary classifiers, in the future the scope might be extended.
• For large datasets, performing hyperparameter optimization can be very computationally expensive. For gradient boosted tree models, one alternative is to use early stopping of the training step. For this use the parameters early_stopping_rounds and eval_metric.

Setup the dataset¶

In order to use the functionality, let's set up an example dataset with:

• 18 numerical features
• 1 static feature
• 1 static feature
• 1 feature with missing values

In [1]:

%%capture
!pip install probatus
!pip install lightgbm

%%capture !pip install probatus !pip install lightgbm

In [2]:

import lightgbm
import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.model_selection import RandomizedSearchCV

from probatus.feature_elimination import ShapRFECV

feature_names = [
    "f1",
    "f2_missing",
    "f3_static",
    "f4",
    "f5",
    "f6",
    "f7",
    "f8",
    "f9",
    "f10",
    "f11",
    "f12",
    "f13",
    "f14",
    "f15",
    "f16",
    "f17",
    "f18",
    "f19",
    "f20",
]

# Prepare two samples
X, y = make_classification(
    n_samples=1000,
    class_sep=0.05,
    n_informative=6,
    n_features=20,
    random_state=0,
    n_redundant=10,
    n_clusters_per_class=1,
)
X = pd.DataFrame(X, columns=feature_names)

# Make missing nr consistent
np.random.seed(42)
X["f2_missing"] = X["f2_missing"].apply(lambda x: x if np.random.rand() < 0.8 else np.nan)
X["f3_static"] = 0

import lightgbm import numpy as np import pandas as pd from sklearn.datasets import make_classification from sklearn.model_selection import RandomizedSearchCV from probatus.feature_elimination import ShapRFECV feature_names = [ "f1", "f2_missing", "f3_static", "f4", "f5", "f6", "f7", "f8", "f9", "f10", "f11", "f12", "f13", "f14", "f15", "f16", "f17", "f18", "f19", "f20", ] # Prepare two samples X, y = make_classification( n_samples=1000, class_sep=0.05, n_informative=6, n_features=20, random_state=0, n_redundant=10, n_clusters_per_class=1, ) X = pd.DataFrame(X, columns=feature_names) # Make missing nr consistent np.random.seed(42) X["f2_missing"] = X["f2_missing"].apply(lambda x: x if np.random.rand() < 0.8 else np.nan) X["f3_static"] = 0

In [3]:

# First 5 rows of first 5 columns
X[feature_names[:5]].head()

# First 5 rows of first 5 columns X[feature_names[:5]].head()

Out[3]:

	f1	f2_missing	f3_static	f4	f5
0	3.399287	-3.902230	0	0.037207	-0.211075
1	-2.480698	NaN	0	0.302824	0.729950
2	-0.690014	1.350847	0	1.837895	-0.745689
3	-5.291164	4.559465	0	-1.277930	3.688404
4	-1.028435	NaN	0	-0.576209	-0.790525

Set up the model and model tuning¶

You need to set up the model that you would like to use in the feature elimination. probatus requires a tree-based or linear binary classifier in order to speed up the computation of SHAP feature importance at each step. We recommend using LGBMClassifier, which by default handles missing values and categorical features.

The example below applies randomized search in order to optimize the hyperparameters of the model at each iteration of the search.

In [4]:

model = lightgbm.LGBMClassifier(max_depth=5, class_weight="balanced")

param_grid = {
    "n_estimators": [5, 7, 10],
    "num_leaves": [3, 5, 7, 10],
}
search = RandomizedSearchCV(model, param_grid)

model = lightgbm.LGBMClassifier(max_depth=5, class_weight="balanced") param_grid = { "n_estimators": [5, 7, 10], "num_leaves": [3, 5, 7, 10], } search = RandomizedSearchCV(model, param_grid)

Apply ShapRFECV¶

Now let's apply the ShapRFECV.

In [5]:

shap_elimination = ShapRFECV(model=search, step=0.2, cv=10, scoring="roc_auc", n_jobs=3)
report = shap_elimination.fit_compute(X, y)

shap_elimination = ShapRFECV(model=search, step=0.2, cv=10, scoring="roc_auc", n_jobs=3) report = shap_elimination.fit_compute(X, y)

[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000790 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000873 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000773 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000880 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000959 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000971 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000981 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000593 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000820 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000760 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000681 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000940 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000815 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000094 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000830 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000830 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000839 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000092 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Info] Start training from score -0.000000

[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000782 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000081 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000099 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000147 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 765
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 3
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000164 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 510
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 2
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000086 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000108 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000131 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000164 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000145 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000753 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000900 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000708 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000657 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 255
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 1
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000

At the end of the process, you can investigate the results for each iteration.

In [6]:

# First 5 rows of first 5 columns
report[["num_features", "features_set", "val_metric_mean"]]

# First 5 rows of first 5 columns report[["num_features", "features_set", "val_metric_mean"]]

Out[6]:

	num_features	features_set	val_metric_mean
1	20	[f1, f2_missing, f3_static, f4, f5, f6, f7, f8...	0.904975
2	16	[f1, f2_missing, f5, f7, f8, f9, f10, f11, f12...	0.923160
3	13	[f1, f5, f8, f9, f10, f11, f12, f14, f15, f16,...	0.923200
4	11	[f5, f8, f9, f10, f11, f14, f15, f16, f18, f19...	0.923381
5	9	[f5, f8, f9, f11, f14, f15, f16, f19, f20]	0.931904
6	8	[f5, f8, f9, f14, f15, f16, f19, f20]	0.910296
7	7	[f5, f8, f9, f14, f15, f16, f19]	0.919259
8	6	[f5, f9, f14, f15, f16, f19]	0.910097
9	5	[f9, f14, f15, f16, f19]	0.888632
10	4	[f9, f14, f16, f19]	0.879369
11	3	[f9, f16, f19]	0.869466
12	2	[f16, f19]	0.817814
13	1	[f16]	0.720609

Once the process is completed, you can visualize the results.¶

Let's investigate the performance plot. In this case, the Validation AUC score has the highest Validation AUC at 11 features and a peak at 6 features.

In [7]:

performance_plot = shap_elimination.plot()

performance_plot = shap_elimination.plot()

Let's see the final feature set:

In [8]:

shap_elimination.get_reduced_features_set(num_features=6)

shap_elimination.get_reduced_features_set(num_features=6)

Out[8]:

array(['f5', 'f9', 'f14', 'f15', 'f16', 'f19'], dtype=object)

You can also provide a list of features that should not be eliminated. Say based on your prior knowledge you know that the features f10,f19,f15 are important and should not be eliminated. This can be done by providing a list of columns to columns_to_keep parameter in the fit() function.

In [9]:

shap_elimination = ShapRFECV(model=search, step=0.2, cv=10, scoring="roc_auc", n_jobs=3, min_features_to_select=4)
report = shap_elimination.fit_compute(X, y, columns_to_keep=["f10", "f15", "f19"])

performance_plot = shap_elimination.plot()

shap_elimination = ShapRFECV(model=search, step=0.2, cv=10, scoring="roc_auc", n_jobs=3, min_features_to_select=4) report = shap_elimination.fit_compute(X, y, columns_to_keep=["f10", "f15", "f19"]) performance_plot = shap_elimination.plot()

[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000633 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000654 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4299
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4294
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4293
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4335
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4321
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4322
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4322
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4320
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000733 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4326
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000829 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4322
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4321
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4322
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4321
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4321
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 17
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3789
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3784
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3783
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3825
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3811
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000661 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3812
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3812
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3810
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000634 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3816
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000778 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3812
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3811
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3812[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3811
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3811
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000688 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000684 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2550
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000112 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005046 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005706 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003726 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2040
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 8
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000729 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1785
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 7
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1530
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 6
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1275
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 5
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000

Let's see the final feature set:

In [10]:

shap_elimination.get_reduced_features_set(num_features=4)

shap_elimination.get_reduced_features_set(num_features=4)

Out[10]:

array(['f10', 'f15', 'f16', 'f19'], dtype=object)

Early Stopping ShapRFECV¶

Early stopping is a type of regularization, common in gradient boosted trees. Supported packages are: LightGBM, XGBoost and CatBoost. It consists of measuring how well the model performs after each base learner is added to the ensemble tree, using a relevant scoring metric. If this metric does not improve after a certain number of training steps, the training can be stopped before the maximum number of base learners is reached.

Early stopping is thus a way of mitigating overfitting in a relatively cheaply, without having to find the ideal regularization hyperparameters. It is particularly useful for handling large datasets, since it reduces the number of training steps which can decrease the modelling time.

Early Stopping requires parameters early_stopping_rounds eval_metric in ShapRFECV class and at the moment only supports the three aforementioned libraries. See the example below how to use it with LightGBM.

In [15]:

%%timeit -n 10
from probatus.feature_elimination import ShapRFECV

model = lightgbm.LGBMClassifier(n_estimators=200, max_depth=3)

# Run feature elimination
shap_elimination = ShapRFECV(
    model=search, step=0.2, cv=10, scoring="roc_auc", eval_metric="auc", early_stopping_rounds=5, n_jobs=3
)
report = shap_elimination.fit_compute(X, y)

%%timeit -n 10 from probatus.feature_elimination import ShapRFECV model = lightgbm.LGBMClassifier(n_estimators=200, max_depth=3) # Run feature elimination shap_elimination = ShapRFECV( model=search, step=0.2, cv=10, scoring="roc_auc", eval_metric="auc", early_stopping_rounds=5, n_jobs=3 ) report = shap_elimination.fit_compute(X, y)

[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001848 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001018 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000711 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000814 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000805 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000890 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000860 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000

[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000798 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000855 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000929 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000752 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000643 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000804 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000157 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000812 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000527 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000838 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metricDid not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599

Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000900 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000704 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000808 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001000 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001044 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000726 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000832 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Total Bins 3315

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000116 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001053 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000600 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000949 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000695 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
[LightGBM] [Warning] Unknown parameter: eval_metricEvaluated only: binary_logloss

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000128 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
Training until validation scores don't improve for 5 rounds[LightGBM] [Info] Start training from score -0.000000

Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001015 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000696 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Info] Total Bins 2295

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000944 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000830 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000470 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000828 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000665 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000543 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000527 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000470 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000697 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000829 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000871 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Start training from score -0.000000[LightGBM] [Info] Total Bins 4067

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004153 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738Evaluated only: binary_logloss
Evaluated only: binary_logloss

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000143 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000621 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000741 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001451 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453851
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000684 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000788 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468838
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000797 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006124 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Total Bins 3315

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000713 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000759 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000872 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000562 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000460 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000719 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000696 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000460 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000655 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Total Bins 2295

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000816 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000830 seconds.
You can set `force_col_wise=true` to remove the overhead.
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000904 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Start training from score -0.000000

Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000962 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000983 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453851
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000880 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468838
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000780 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453851
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006011 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468838
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000812 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000773 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000702 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000591 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001063 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000899 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003323 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000778 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000752 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000877 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001116 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000760 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000861 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001975 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000565 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000733 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000837 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000428 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000649 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001000 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000787 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000796 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000152 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001564 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000715 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000693 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000877 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000109 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000591 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003007 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002096 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000673 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000721 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000857 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.467541
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.47876
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.493516
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000712 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.53103
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50123
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473724
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.512086
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.558181
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.509927
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.517469
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000788 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000527 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45236
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.449904
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506543
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479103
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.449827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000776 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000802 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.487422
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.525683
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.487804
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490101
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000949 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001039 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000847 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003092 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000582 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000621 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000743 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001570 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000876 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000527 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001626 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000761 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002076 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000735 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001543 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005052 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000527 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000710 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000799 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002857 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001006 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000696 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000731 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000661 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000618 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001683 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000649 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000952 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Info] Total Bins 4066

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000504 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003842 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002040 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000858 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006107 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000643 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000880 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000160 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000872 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001011 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001004 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001906 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000664 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000716 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000467 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000645 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000857 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000733 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000888 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000702 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000649 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001208 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000543 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001225 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000904 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000663 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001015 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000722 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000691 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005663 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000559 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000101 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000782 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002960 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001745 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000158 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003990 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 roundsTraining until validation scores don't improve for 5 rounds

Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.501179
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.513522
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.497389
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000890 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.525775
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.540561
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.50506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000669 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000619 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.531653
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.569797
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.524422
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.546486
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000621 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002633 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001008 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000530 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000748 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000527 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000467 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000688 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000564 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000792 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000968 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001040 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000975 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000930 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000733 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000494 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002543 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000873 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.016650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001530 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006861 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001664 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000751 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000834 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000796 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000848 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000747 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000763 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000Training until validation scores don't improve for 5 rounds

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000748 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000745 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000778 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000459 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000864 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001144 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000158 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002888 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000868 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000932 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000654 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000748 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000995 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000570 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000807 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000672 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000552 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001966 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000655 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001013 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000634 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001170 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000828 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Total Bins 2295

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000697 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000818 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.507092
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500083
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508151
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.55021
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.522494
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506735
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.528802
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.573106
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527028
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.545238
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454783
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000742 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000731 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000901 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000979 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000938 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001150 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000719 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000619 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000799 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000144 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000765 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003735 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001521 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000985 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827

Evaluated only: binary_logloss
[LightGBM] [Info] Total Bins 4830
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000600 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000783 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000735 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000756 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13

[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000678 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000467 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000841 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000640 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds[LightGBM] [Warning] Unknown parameter: eval_metric

Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000068 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000735 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000724 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000640 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001726 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001669 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001152 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000601 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001087 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000986 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000699 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000909 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001033 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000428 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000156 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003048 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000651 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000618 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000802 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000752 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001887 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001001 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007133 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000159 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000143 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000780 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000750 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metricDid not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404

Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000708 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000711 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000889 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000494 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003046 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000754 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000673 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000848 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000905 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001833 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001132 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000456 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001141 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001019 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001688 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000814 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000832 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000525 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000711 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000793 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000774 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000713 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000868 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.013714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000733 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000638 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001002 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001574 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000719 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000709 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000750 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001010 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000817 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000165 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002080 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000622 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003813 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000570 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000768 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002593 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001938 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13

[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000695 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000767 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000978 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001119 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000972 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001772 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000833 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000162 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000716 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000655 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000789 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000807 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000756 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000662 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000952 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032904 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000797 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000719 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000991 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001709 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000827 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000665 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006663 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000846 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000988 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000837 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000597 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000672 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005973 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001380 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000688 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000949 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000768 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001020 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001823 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000768 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001113 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002112 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000747 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000685 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000721 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000628 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000787 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000565 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453851
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000902 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468838
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000874 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000769 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000985 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001823 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000428 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000649 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000902 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000Training until validation scores don't improve for 5 rounds

[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000562 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000734 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000731 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000922 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000618 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001989 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000995 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001611 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000879 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000994 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001072 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006569 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008845 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 roundsTraining until validation scores don't improve for 5 rounds

Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000924 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000977 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000475 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005754 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000904 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000831 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000805 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000696 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000781 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000802 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.021244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.018936 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002881 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000893 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000956 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000759 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000735 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453851
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468838
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000611 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014012 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007740 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001723 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001883 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of positive: 445, number of negative: 455
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000885 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000908 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000661 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000964 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000852 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000716 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000946 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000909 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000774 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000698 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000669 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000830 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000872 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Total Bins 4067[LightGBM] [Info] Total Bins 4066

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000696 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001910 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000841 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000664 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003103 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000628 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000982 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000673 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Start training from score 0.000000

[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000767 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002428 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000881 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000683 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000662 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000707 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000799 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000970 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001095 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000981 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000901 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001069 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000824 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003251 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000651 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002618 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001013 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001840 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000736 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000786 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000756 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000808 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000834 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000708 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000895 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000710 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000655 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000618 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001074 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000942 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000562 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003646 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003148 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001006 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000916 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000726 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001056 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.014095 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000145 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001187 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000590 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001286 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000740 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000575 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000769 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000863 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000929 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008854 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000684 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000870 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000668 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000792 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001670 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13[LightGBM] [Info] Start training from score -0.000000

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000600 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds[LightGBM] [Info] Total Bins 2805

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000699 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001044 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000914 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.015689 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002088 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000665 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000950 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000600 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000924 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000664 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000880 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001073 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000878 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003043 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000641 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000645 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000575 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000390 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metricDid not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827

Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006409 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000601 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000419 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000634 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.514732
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508126
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.542928
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.51677
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.502114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.531653
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.557732
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.531758
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.528743
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003804 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000672 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000798 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000743 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001011 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000898 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000708 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001021 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000764 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000634 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000856 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000504 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001766 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000124 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000551 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.015345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002938 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000584 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001829 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005344 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000590 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000665 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000491 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000762 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000462 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000790 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001061 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000964 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000721 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000754 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009081 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001150 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002795 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007574 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001166 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000792 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008078 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000606 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000470 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001621 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000838 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds[LightGBM] [Info] Number of positive: 444, number of negative: 456

[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007748 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000669 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002615 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000779 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000974 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001134 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000570 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000590 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000752 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000897 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001149 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002113 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000375 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000411 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000562 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006601 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006493 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004834 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001131 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000559 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000743 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000774 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000518 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000761 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001088 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001033 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000848 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000724 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000806 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metricTraining until validation scores don't improve for 5 rounds

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000876 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001996 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000979 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000669 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001023 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000832 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000997 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000575 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000919 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000729 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000768 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004963 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000849 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014832 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000702 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000626 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000793 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001932 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metricTraining until validation scores don't improve for 5 rounds

[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000517 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000780 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000710 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000

[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000693 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000767 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000776 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001610 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000638 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000636 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000782 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010946 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.013417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000755 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000791 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000738 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000759 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000801 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000953 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001000 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000858 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000088 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000798 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000523 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000503 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000840 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004769 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.027086 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000463 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000892 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003108 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001898 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000540 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000781 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.023069 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000639 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000674 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000869 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000771 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001554 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000467 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000858 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000943 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005590 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000379 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000174 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001717 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000886 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000744 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000800 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000712 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000428 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000945 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000909 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000849 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000391 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.015118 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000820 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000886 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000780 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000757 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001048 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000603 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000518 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000483 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000707 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000598 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000784 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000395 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010992 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000584 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000823 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000676 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000608 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000841 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006694 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000421 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002949 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000649 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000477 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000430 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.016898 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000412 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000812 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000518 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000859 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000910 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000891 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000805 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009715 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000855 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001114 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000582 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000700 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000684 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003666 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000672 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 roundsTraining until validation scores don't improve for 5 rounds

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000904 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001139 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000534 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000673 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003099 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000868 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006596 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000434 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001026 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000509 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000596 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003774 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001600 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000687 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000776 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000933 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000447 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000911 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000940 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001039 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000600 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000408 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000490 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010954 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001935 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000846 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000479 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000105 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000699 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000460 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001140 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000601 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.013175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001100 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000569 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Start training from score -0.000000

Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003950 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000580 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000702 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000780 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000820 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000361 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000420 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000612 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000883 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000690 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000852 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001673 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000602 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000616 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000527 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000790 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000444 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002927 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001075 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000787 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001030 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000382 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000585 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000484 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000599 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002968 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001024 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000619 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000763 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000688 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000717 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000657 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000836 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000952 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012658 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000364 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000502 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000542 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001064 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000413 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002760 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000672 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000377 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001265 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001366 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000648 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000653 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000394 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.017577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000574 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000360 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001690 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000631 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000730 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000777 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000504 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000558 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002645 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000584 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000776 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453851
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002967 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001561 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468838
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Info] Number of positive: 445, number of negative: 455

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000769 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000842 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000878 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000972 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000843 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506604
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500083
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000384 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508151
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.55021
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.522604
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506758
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001047 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001154 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.528802
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.573106
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527028
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.54533
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000847 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000423 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001002 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001891 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009127 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004047 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000629 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000628 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000654 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000733 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011310 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000476 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000814 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000645 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000519 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000346 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000522 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000724 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000608 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000417 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000554 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001654 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000351 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000416 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000410 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506604
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003124 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500083
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508151
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.55021
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001060 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001014 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.522604
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.528802
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506758
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000878 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000891 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527028
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.573106
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.54533
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.017982 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000389 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001519 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000656 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000582 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000449 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001009 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000129 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003504 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000630 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000938 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000722 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004414 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000899 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000775 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000829 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000789 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000835 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000338 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000624 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000453 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 444, number of negative: 456[LightGBM] [Info] Total Bins 4066

[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000371 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000810 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000620 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000405 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000662 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000664 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005787 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.022315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000398 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000761 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001633 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000769 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000737 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000701 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000450 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000494 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000565 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000733 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000650 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000325 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009361 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000560 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001372 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000609 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000703 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000471 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000992 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001104 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000932 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000472 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.042896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.013909 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000975 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000878 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001100 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000902 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000907 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000436 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000500 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000392 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000533 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000564 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000437 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000452 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001093 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.506872
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000567 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.500341
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001683 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001769 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.510905
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.553326
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.527935
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000751 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000627 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.508066
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.53096
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.570323
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000376 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.529772
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[7]	valid_0's binary_logloss: 0.530623
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000890 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000839 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000573 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002853 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000878 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000646 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001080 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000891 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000793 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000355 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000594 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006363 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000795 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000862 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000803 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000984 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000822 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001146 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000770 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000860 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000511 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000642 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000438 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000824 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000525 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000555 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000582 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000903 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001021 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000974 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000808 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455Training until validation scores don't improve for 5 rounds

Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002586 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014494 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000359 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000682 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000613 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000600 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001003 seconds.
You can set `force_col_wise=true` to remove the overhead.
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000528 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001017 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000849 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000508 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000402 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000328 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000388 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000445 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002735 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000177 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000176 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000180 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478132
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009711 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479744
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489248
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.524494
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000341 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000526 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.496168
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475717
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.51176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000850 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000924 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.548114
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.505506
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.518291
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000926 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000564 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000403 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000432 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000516 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000380 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000728 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000727 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000617 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000536 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000425 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000568 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000912 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000685 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.013895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000751 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000644 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000507 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000Training until validation scores don't improve for 5 rounds

[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000343 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000192 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000185 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005113 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000493 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000400 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000178 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000175 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000161 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000370 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000171 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000189 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000652 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000663 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000373 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000442 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000397 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000539 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000889 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000695 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009875 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000473 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 roundsTraining until validation scores don't improve for 5 rounds

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001131 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000794 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000576 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000718 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000671 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000693 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000451 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000347 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001628 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000506 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000773 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453851
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000512 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000918 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468838
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000894 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000769 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000

[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000496 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011759 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001111 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003614 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000861 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000384 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001089 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000758 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000557 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000458 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000340 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000344 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000619 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000427 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000566 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000813 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000858 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000393 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000811 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000605 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000616 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000455 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000320 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000315 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000345 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000553 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000337 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001556 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000365 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000778 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000875 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001018 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001565 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001581 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000635 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000354 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000459 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000367 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000616 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000334 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004380 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000383 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000515 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000406 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000518 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.007077 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000550 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000494 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000199 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000933 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000637 seconds.
You can set `force_col_wise=true` to remove the overhead.[LightGBM] [Info] Number of positive: 444, number of negative: 456

[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001358 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000705 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001027 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000973 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000481 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005031 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.015489 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000922 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000374 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000208 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000428 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000675 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45176
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.453382
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.48376
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003459 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000974 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000865 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50328
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483615
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.456453
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000781 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000745 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001062 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.481258
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485197
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526954
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.503875
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.013844 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000193 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000183 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000213 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000215 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005396 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008720 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000732 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000739 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000864 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000363 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000937 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000336 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.012774 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000270 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000301 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000303 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000579 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000464 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000537 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000895 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000548 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000401 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001394 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.032928 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000362 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000323 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000273 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000296 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000352 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000487 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000342 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000446 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000749 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000874 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000632 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000876 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000466 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000283 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000333 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000495 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473584
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490241
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000292 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000726 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000647 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000520 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.528974
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498454
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.470442
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000828 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000826 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000770 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513266
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.556948
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515432
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.515026
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000867 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000209 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000221 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000230 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000339 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000532 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000179 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.467541
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.47876
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000917 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.493516

Evaluated only: binary_logloss
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000623 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.53103
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.50123
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.473724
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000657 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000369 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000677 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.512086
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.558181
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.509927
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.517469
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000331 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000272 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000245 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000232 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000202 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000187 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000206 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45236
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000365 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.449904
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000198 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.478878
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506543Training until validation scores don't improve for 5 rounds

Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479103
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000414 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000399 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.449827
Evaluated only: binary_logloss
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.487422
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.525683
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.487804
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.490101
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000324 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002809 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000667 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000289 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000313 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000276 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000291 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000327 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000350 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000795 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000314 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000290 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000274 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000329 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000385 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000531 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000288 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000353 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4809
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000317 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4804
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000319 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4803
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000286 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4845
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002424 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000887 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002977 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000719 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4830
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000680 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4836
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000828 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752

Evaluated only: binary_loglossEvaluated only: binary_logloss

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452854
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000659 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000679 seconds.
You can set `force_col_wise=true` to remove the overhead.

[LightGBM] [Info] Total Bins 4832
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000751 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric[LightGBM] [Warning] Unknown parameter: eval_metric

[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000540 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4831
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 19
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506404
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001519 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000529 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000233 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000293 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000228 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000200 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000299 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000583 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000252 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000247 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000246 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000264 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000298 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000349 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000282 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000281 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4040
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4044
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4039
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4038
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000279 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4080
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 16
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000546 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.45434
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000851 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000535 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4065
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000485 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4071
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000441 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468752
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000582 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001098 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4067
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000957 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.484183
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4066
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 16
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506513
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000258 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000267 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.014545 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000426 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000224 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000196 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000191 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000201 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000335 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000461 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001170 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000249 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000250 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000236 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000212 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000261 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000305 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000210 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000241 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000248 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000311 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000308 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454296
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011356 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005167 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005795 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.454006
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.465827
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.513013
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000714 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000592 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000896 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.468719
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452793
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.476738
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000706 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000686 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000914 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 3315
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 13
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.534713
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.483996
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.506466
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000220 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000589 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000433 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000229 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000237 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000218 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000216 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000297 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000488 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000239 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000262 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000821 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000204 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000310 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000480 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000256 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000243 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000266 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000257 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000284 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000181 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000219 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000285 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000304 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000309 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000348 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000205 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000255 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 11
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000278 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000265 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000454 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.457678
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.447269
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.462973
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000190 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000577 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514447
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.482835
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.452599
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000482 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000521 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.480688
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.526858
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.485531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2805
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 11
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.486894
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005578 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000148 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000151 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000254 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000194 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000172 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000197 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000251 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000214 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000263 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001996 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000268 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000222 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000217 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000211 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000227 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000226 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000269 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000207 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000234 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000242 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000386 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002778 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000203 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000295 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000260 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000223 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000332 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000357 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000271 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000154 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000186 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000240 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000422 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000418 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 395, number of negative: 405
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000253 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
[LightGBM] [Info] Number of positive: 396, number of negative: 404
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000188 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 800, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Info] Number of positive: 494, number of negative: 506
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000238 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 9
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.459424
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000607 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.446305
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.479313
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 444, number of negative: 456
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000435 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000
[LightGBM] [Info] Start training from score 0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000819 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000595 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.491277
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.469163
Evaluated only: binary_logloss
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000478 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.448531
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005672 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003054 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.475492
Evaluated only: binary_logloss
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.489766
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.514635
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] Number of positive: 445, number of negative: 455
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000330 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2295
[LightGBM] [Info] Number of data points in the train set: 900, number of used features: 9
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000
[LightGBM] [Info] Start training from score -0.000000
Training until validation scores don't improve for 5 rounds
Did not meet early stopping. Best iteration is:
[10]	valid_0's binary_logloss: 0.498824
Evaluated only: binary_logloss
[LightGBM] [Warning] Unknown parameter: eval_metric
[LightGBM] [Warning] Unknown parameter: eval_metric
2.92 s ± 142 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [12]:

# Make plots
performance_plot = shap_elimination.plot()

# Get final feature set
final_features_set = shap_elimination.get_reduced_features_set(num_features=9)

# Make plots performance_plot = shap_elimination.plot() # Get final feature set final_features_set = shap_elimination.get_reduced_features_set(num_features=9)

As it is hinted in the example above, with large datasets and simple base learners, early stopping can be a much faster alternative to hyperparameter optimization of the ideal number of trees.

Note that although Early Stopping ShapRFECV supports hyperparameter search models as input, early stopping is used only during the Shapley value estimation step, and not during hyperparameter search.